While many companies are able to measure the overall effectiveness of promotions, measuring at the per-campaign or event level is much more difficult and has become somewhat of a Holy Grail for marketers.

We recently worked with a large consumer goods manufacturer to tackle this thorny problem. Here's how we solved it.

The client is a large paper and paper goods manufacturer with several divisions, including one focused on consumer goods. The marketing team regularly measured and reported on ROI and marketing effectiveness at an aggregate level, but had not cracked the code on how to measure individual campaigns. That made it impossible to identify and repeat the most effective campaigns, and ditch the ineffective ones.

One impediment to doing so was that the internal shipment data was not aligned with promotional and sales data. This made it impossible for the marketers to see the relationship between promotions and orders. Another was that brand teams' estimations of ROI were never comparable due to inconsistent methodologies.

The company's senior leadership wanted to remove those barriers and leverage consistent analytics approaches to better measure and manage marketing effectiveness. The team wanted to develop a consistent measurement framework that would create a common language across the organization, with the ultimate objective of gaining a more accurate assessment of promotional effectiveness and allocation of costs to the right campaign events.

Ensure you're looking at the correct problem, not just symptoms. We call this 'exploring the DNA' of a problem, and it helps determine the contributing factors. In this case there were multiple factors, including misalignment of shipment and consumption, consumer/customer forward buy and internal cannibalization. Adjusting any of these would impact the others. Think of a water balloon-if you press in one spot, the balloon will bulge out in another. Hence, it's important that we understand the relations among them.

Isolate the base and incremental data to get a true sense of sales driven by promotions versus regular ongoing business. This is essential, as business sometimes is not driven by promotions at all. Identify the base by calculating the returns on everyday discount and deducting the resulting figure from total sales to isolate incremental sales. This lets you compare the returns on trade investments with other channels of investment, such as media and online, to help optimize the allocation of future investments in the consumer segment.

Isolate the effects of overlapping promotions and cannibalization to get to the true drivers for purchase. We combined overlapping events to form a consolidated event by essentially combining the investments and consumption volume during the event period. We accounted for internal cannibalization through a model that estimated the weekly volume cannibalized from internal brands during events. This helps identify cases of high cannibalization, and hence products with high interaction. The event scheduling team can then use these cases to avoid coinciding events for these products.

Align shipment data to each campaign based on the timing, duration and scope of the promotion to compute the incremental lift attributed to the promotion.

Using the output of these exercises, you can build a model encased in a self-service dashboard. Marketers and analysts can use this dashboard to gain views into product, promotion and retailer dimensions, with the ability to drill down into each.

As the author of ‘Profiting from The Magic of Marketing Metrics’ (an e-book published by and for sale from Target Marketing), I wish that I had been able to include some of your insights. What would be extremely valuable now would be for you to share your actual models to readers could add to them with the resulting enrichment of everyone.